Qevlar AI Raises $30M to Transform Security Operations Centers With Autonomous AI

Cybersecurity startup Qevlar AI has raised $30 million in new funding to expand its autonomous AI platform designed to transform how Security Operations Centers (SOCs) handle threats and improve overall security posture.

The round was jointly led by Partech and Forgepoint Capital International, with participation from EQT Ventures & Growth, as enterprises increasingly look for ways to manage overwhelming volumes of security alerts without dramatically expanding headcount.

Founded in 2023, Qevlar AI develops an AI-driven SOC platform that autonomously investigates security alerts, correlates evidence across tools, and determines whether incidents are malicious or benign. The company’s technology is designed to help analysts move beyond repetitive investigation tasks and focus on strategic defense initiatives.

Tackling the Alert Overload Problem

Security teams face a growing operational challenge. Modern infrastructure generates massive volumes of alerts across security information and event management (SIEM), endpoint detection and response (EDR), and other security tools.

Investigating these alerts often consumes the majority of a SOC’s time. Qevlar AI’s platform aims to remove that bottleneck by autonomously enriching data, identifying patterns, and generating investigation reports.

According to the company, organizations deploying the platform have seen:

• A 10× reduction in investigation time, bringing analysis down to roughly three minutes
• 24/7 automated investigation coverage
• 100% of alerts investigated with full context
• The ability for analysts to focus on threat hunting and incident response rather than routine triage

This shift is intended to move SOC teams from reactive alert handling toward proactive security posture improvements.

Moving From Alert Handling to Security Insights

While many AI-powered security tools focus primarily on alert triage, Qevlar AI is positioning its platform as a broader intelligence layer for SOC operations.

The system automatically investigates alerts and then analyzes patterns across those investigations to identify underlying vulnerabilities, misconfigurations, or recurring attack techniques. These insights can help security teams address root causes and reduce the number of alerts generated over time.

Ahmed Achchak, co-founder and CEO of Qevlar AI, says the company’s goal is to fundamentally change how SOC performance is measured.

Rather than tracking how quickly alerts are resolved, he argues that the more meaningful metric is whether security teams are actually improving their organization’s security posture by understanding why incidents occur and preventing them from repeating.

Growing Adoption Across Enterprises and MSSPs

Qevlar AI reports strong adoption among both large enterprises and managed security service providers (MSSPs). Customers include global organizations such as Mercedes-Benz and Sodexo, alongside cybersecurity service providers including Orange Cyberdefense, ECI, and Atos.

These organizations are increasingly adopting AI-driven SOC platforms as security teams struggle with the growing complexity of modern IT environments and a global shortage of cybersecurity talent.

The platform integrates with existing security tools, automatically collecting and correlating data to determine whether alerts represent real threats. It can then produce structured investigation reports and recommend remediation actions for analysts.

Expanding the Autonomous SOC Vision

With the new funding, Qevlar AI plans to accelerate development of its autonomous AI SOC platform, expanding its capabilities beyond investigation to include deeper detection and remediation workflows.

Investors say the company’s approach reflects a broader shift underway in cybersecurity: moving from rule-based automation toward autonomous AI agents capable of conducting investigations independently and learning from patterns across incidents.

As security environments continue to grow more complex, platforms that reduce manual workloads while surfacing actionable insights are becoming increasingly central to enterprise cyber defense strategies.